As more people turn to AI-powered answer engines like Perplexity, ChatGPT, and Claude for research and recommendations, marketers face a new problem: their websites may rank well in Google but remain invisible in AI-generated responses. Two newly launched platforms are now helping enterprises measure and improve their visibility in generative search, signaling a fundamental shift in how digital discovery works.

Why Are Brands Disappearing From AI Search Results?

The challenge is more subtle than a simple ranking problem. When a user asks an AI system a question, the engine doesn't just search for one answer. Instead, it expands the original query into multiple related sub-questions, gathering information from several sources before synthesizing a response. A search for "best enterprise SEO software," for example, may trigger AI searches about pricing, features, security requirements, customer reviews, and competitor comparisons.

A website might rank highly for the main keyword but still be excluded from the AI-generated answer if it doesn't contain the specific passages, comparisons, or evidence the engine needs to answer those related questions. This gap between traditional search visibility and AI search visibility is now a critical business problem for large enterprises.

"The brands feeling this shift first are large enterprises, the ones with the most to lose when an AI answer leaves them out," said Anirudh Singla, Founder and CEO of Pepper.

Anirudh Singla, Founder and CEO of Pepper

What Tools Are Marketers Using to Track AI Visibility?

Two new platforms launched in July 2026 are addressing this gap directly. QueryFanOuts, created by marketing engineer Paul Andre de Vera, is a free tool that transforms Google Search Console data into a content roadmap for AI search. The platform identifies the sub-questions and related searches that answer engines like Perplexity, ChatGPT, and Google AI Overviews may explore before generating a response.

Pepper's GEO platform, meanwhile, takes a broader approach. Built through work with over 250 enterprises, the platform analyzes how brands appear across multiple AI search engines, including ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews. The system has analyzed more than 10 million prompts across major generative search engines and tracks which sources are being cited in AI-generated responses.

How to Optimize Your Content for AI Search Engines

Both platforms reveal a new content strategy that differs from traditional SEO. Instead of optimizing a single page for one keyword, marketers now need to create interconnected content that answers the full research journey an AI system might take. Here are the key approaches both platforms recommend:

• Expand related topics: Identify sub-questions and related searches connected to your main keyword, then create dedicated sections or pages that answer each one comprehensively.
• Use declarative headings: Write headings that clearly state what each section covers, making it easier for AI systems to extract and cite specific passages.
• Provide self-contained passages: Structure content so individual sections can stand alone and answer specific questions, rather than requiring readers to piece together information across multiple pages.
• Include specific evidence: Add examples, data, comparisons, and supporting facts that AI systems may need to validate claims and provide context in their responses.
• Create pillar and supporting content: Develop a main pillar page on a broad topic, then create focused supporting pages for sub-queries, FAQs, and comparison content.

QueryFanOuts helps marketers identify these opportunities by converting raw Google Search Console data into actionable recommendations. The tool requires no tracking pixel, website crawler, or paid subscription. Users simply export their Queries report from Google Search Console, upload it to QueryFanOuts, and receive an organized analysis within seconds.

Pepper's platform takes a competitive angle, showing not only whether a brand appears in AI responses but also which third-party websites, competitor pages, and review platforms are being cited instead. This allows marketing teams to understand which sources are shaping how their brand is represented in AI-generated answers.

What Metrics Matter Most in AI Search?

Pepper's platform evaluates three primary dimensions of AI search visibility. The first is whether an AI model mentions the brand in response to a relevant query. The second is whether the model cites the brand's owned website as a source. The third is whether the brand appears consistently across multiple engines, prompt variations, and buyer contexts.

These signals help enterprises distinguish between isolated visibility and sustained presence across generative search. A brand might appear once in a ChatGPT response but be missing from Perplexity or Claude, indicating an inconsistent presence that needs attention.

QueryFanOuts focuses on a different metric: content-gap scores. The platform identifies which existing pages should be expanded first, which related questions need dedicated sections, and which search intents deserve separate articles. This helps content teams move from analysis to a writer-ready brief in minutes, rather than spending weeks interpreting spreadsheets.

"Your Search Console data already contains clues about what your audience wants. The problem is that most exports become large spreadsheets with no clear next action. QueryFanOuts decodes that data so a strategist can move from analysis to a writer-ready brief in minutes," said Paul Andre de Vera, creator of QueryFanOuts.

Paul Andre de Vera, Marketing Engineer and Creator of QueryFanOuts

Both platforms reflect a broader shift in digital discovery. As more users rely on generative AI tools for recommendations, comparisons, and category research, traditional search rankings alone no longer guarantee visibility. Marketers who understand how AI systems research and synthesize information will have a significant advantage in the coming years.